Automatic Recognition of Produce Entities from Local Chronicles with Deep Learning
Xu Chenfei1,2,Ye Haiying2,Bao Ping1()
1Institution of Chinese Agricultural Civilization, Nanjing Agricultural University, Nanjing 210095, China 2Economics and Management School, Nantong University, Nantong 226019, China
[Objective] This paper tries to automatically identify the produce aliases, related human figures, places of origin and cited books from ancient local chronicles, aiming to establish a knowledge base for traditional products. [Methods] Firstly, we chose Local Chronicle of Yunnan: Produce as the basic corpus and preprocessed its texts to carry out corpus tagging. Then, we adopted four deep learning models (Bi-RNN, Bi-LSTM, Bi-LSTM-CRF and BERT) to identify the needed entities. Finally, we compared outputs of these models. [Results] The P-value and F-value of the Bi-LSTM model were 5.54% and 3.51% higher than those of the Bi-LSTM-CRF model. The R-value of the BERT model reached 83.36%, which was the best among all models. The Bi-LSTM-CRF model yielded the best results with the entity recognition of cited books (F-value=89.71%), and the BERT model had the best performance on character entities with a F-value of 87.90%. [Limitations] Due to the linguistic characteristics of ancient local chronicles and the domain knowledge required for identifying related entities, there may be errors in tagging. [Conclusions] Deep learning could help us identify needed entities from ancient local chronicles effectively.
徐晨飞, 叶海影, 包平. 基于深度学习的方志物产资料实体自动识别模型构建研究*[J]. 数据分析与知识发现, 2020, 4(8): 86-97.
Xu Chenfei, Ye Haiying, Bao Ping. Automatic Recognition of Produce Entities from Local Chronicles with Deep Learning. Data Analysis and Knowledge Discovery, 2020, 4(8): 86-97.
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